Disease surveillance is a practice by which the spread of disease is monitored in order to predict, observe, and minimize the harm caused by outbreak, epidemic, and pandemic situations. In order to slow down the spread of the disease, location-based restrictions can be implemented that are aimed at limiting the movement of citizens around a geographic location. For instance, restrictions can include forced closures of large and popular public spaces such as airports, universities and market places where people come in contact with each other and the risk of disease transmission is higher.
It is difficult to monitor and understand human mobility and activity during an epidemic. Such efforts commonly require human monitoring, which can be costly. Moreover, the information gathered from human monitoring is often incomplete and not detailed enough to understand nuances of human mobility and behavior. For example, human observation of a restricted location during the restriction might provide some insight as to how many people are moving through the location. However, this high-level and limited sample of information can be incomplete and is ultimately not detailed enough to develop a meaningful understanding of the actual movements and habits of the relevant subsets of individuals in the population in response to the restriction. This lack of meaningful data makes it difficult to preemptively warn individuals in high risk areas, or take preventative measures such as implementing additional restrictions, especially when the infection is a rapidly spreading through the population. Thus, it can be appreciated that current methods for disease surveillance present challenges to identifying effective locations for implementing location-based restrictions on mobility and, once a restriction is in place, evaluating how effective the restrictions are at containing the disease spread (e.g., pandemic/epidemic).
Accordingly, it is desirable for a system and method for accurately identifying suitable locations for imposing a location-based restriction to aid in the containment of an epidemic. It is also desirable for a system and method that can more accurately evaluate the human response to imposed location-based restrictions and quantify the efficacy of the restriction on containment without reliance on individuals to actively gather the information. Furthermore, it is desirable for a system that can detect and monitor the containment of an epidemic in real time or near-real time from an anonymized database of spending records. It should be apparent that, depending on applicable laws and regulations, a consumer can opt in, thereby consenting to the use of their payment records as well as any other personal information he or she provides to the systems for monitoring the containment of an epidemic.
It is with respect to these and other considerations that the disclosure made herein is presented.